
import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
		import tensorflow as tf

		from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

lowercase    :   Union[str, Any]						=			logging.get_logger(__name__)




class 					lowerCamelCase__				(							enum.Enum):




	'''simple docstring'''




	_A								=	0
	_A								=	1




@add_end_docstrings(__lowercase)
class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	_A								=	'generated'







	def __init__(   self						:Any	,  *a						:List[str]	,  **a						:str		)						->					Union[str, Any]:
								super().__init__(*a	,  **a		)

								self.check_model_type(
								    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
								    if self.framework == "tf"
								    else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING		)







	def   _lowerCamelCase							(   self						:Optional[Any]	,  a						:Optional[int]=None	,  a						:Dict=None	,  a						:List[str]=None	,  a						:Dict=None	,  a						:List[Any]=None	,  a						:Optional[Any]=None	,  **a						:str	,  )						->					Tuple:
								__UpperCamelCase      :  Dict       =   {}
								if truncation is not None:
															__UpperCamelCase      :  Tuple       =   truncation

								__UpperCamelCase      :  Optional[int]       =   generate_kwargs

								__UpperCamelCase      :  Dict       =   {}
								if return_tensors is not None and return_type is None:
															__UpperCamelCase      :  List[Any]       =   ReturnType.TENSORS if return_tensors else ReturnType.TEXT
								if return_type is not None:
															__UpperCamelCase      :  Dict       =   return_type

								if clean_up_tokenization_spaces is not None:
															__UpperCamelCase      :  Dict       =   clean_up_tokenization_spaces

								if stop_sequence is not None:
															__UpperCamelCase      :  Any       =   self.tokenizer.encode(a	,  add_special_tokens=a		)
															if len(a		) > 1:
																						warnings.warn(
																						    "Stopping on a multiple token sequence is not yet supported on transformers. The first token of"
																						    " the stop sequence will be used as the stop sequence string in the interim."		)
															__UpperCamelCase      :  Any       =   stop_sequence_ids[0]

								return preprocess_params, forward_params, postprocess_params







	def   _lowerCamelCase							(   self						:int	,  a						:int	,  a						:int	,  a						:int		)						->					Optional[int]:
								return True







	def   _lowerCamelCase							(   self						:int	,  *a						:Union[str, Any]	,  a						:Optional[int]		)						->					Union[str, Any]:
								__UpperCamelCase      :  List[str]       =   self.model.config.prefix if self.model.config.prefix is not None else ""
								if isinstance(args[0]	,  a		):
															if self.tokenizer.pad_token_id is None:
																						raise ValueError("Please make sure that the tokenizer has a pad_token_id when using a batch input"		)
															__UpperCamelCase      :  Optional[Any]       =   ([prefix + arg for arg in args[0]],)
															__UpperCamelCase      :  List[Any]       =   True

								elif isinstance(args[0]	,  a		):
															__UpperCamelCase      :  Tuple       =   (prefix + args[0],)
															__UpperCamelCase      :  List[Any]       =   False
								else:
															raise ValueError(
															    f' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'		)
								__UpperCamelCase      :  str       =   self.tokenizer(*a	,  padding=a	,  truncation=a	,  return_tensors=self.framework		)
								# This is produced by tokenizers but is an invalid generate kwargs
								if "token_type_ids" in inputs:
															del inputs["token_type_ids"]
								return inputs







	def __call__(   self						:Dict	,  *a						:str	,  **a						:str		)						->					Any:
								__UpperCamelCase      :  List[str]       =   super().__call__(*a	,  **a		)
								if (
								    isinstance(args[0]	,  a		)
								    and all(isinstance(a	,  a		) for el in args[0]		)
								    and all(len(a		) == 1 for res in result		)
								):
															return [res[0] for res in result]
								return result







	def   _lowerCamelCase							(   self						:int	,  a						:Dict	,  a						:int=TruncationStrategy.DO_NOT_TRUNCATE	,  **a						:Union[str, Any]		)						->					List[str]:
								__UpperCamelCase      :  int       =   self._parse_and_tokenize(a	,  truncation=a	,  **a		)
								return inputs







	def   _lowerCamelCase							(   self						:Union[str, Any]	,  a						:Union[str, Any]	,  **a						:Union[str, Any]		)						->					Any:
								if self.framework == "pt":
															__UpperCamelCase  ,		__UpperCamelCase      :  Dict       =   model_inputs["input_ids"].shape
								elif self.framework == "tf":
															__UpperCamelCase  ,		__UpperCamelCase      :  Dict       =   tf.shape(model_inputs["input_ids"]		).numpy()

								__UpperCamelCase      :  Dict       =   generate_kwargs.get("min_length"	,  self.model.config.min_length		)
								__UpperCamelCase      :  Any       =   generate_kwargs.get("max_length"	,  self.model.config.max_length		)
								self.check_inputs(a	,  generate_kwargs["min_length"]	,  generate_kwargs["max_length"]		)
								__UpperCamelCase      :  str       =   self.model.generate(**a	,  **a		)
								__UpperCamelCase      :  List[Any]       =   output_ids.shape[0]
								if self.framework == "pt":
															__UpperCamelCase      :  Tuple       =   output_ids.reshape(a	,  out_b // in_b	,  *output_ids.shape[1:]		)
								elif self.framework == "tf":
															__UpperCamelCase      :  str       =   tf.reshape(a	,  (in_b, out_b // in_b, *output_ids.shape[1:])		)
								return {"output_ids": output_ids}







	def   _lowerCamelCase							(   self						:str	,  a						:Dict	,  a						:Optional[Any]=ReturnType.TEXT	,  a						:Dict=False		)						->					int:
								__UpperCamelCase      :  List[str]       =   []
								for output_ids in model_outputs["output_ids"][0]:
															if return_type == ReturnType.TENSORS:
																						__UpperCamelCase      :  Union[str, Any]       =   {f'{self.return_name}_token_ids': output_ids}
															elif return_type == ReturnType.TEXT:
																						__UpperCamelCase      :  Optional[int]       =   {
																						    f'{self.return_name}_text': self.tokenizer.decode(
																						        a	,  skip_special_tokens=a	,  clean_up_tokenization_spaces=a	,  )
																						}
															records.append(a		)
								return records




@add_end_docstrings(__lowercase)
class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	_A								=	'summary'







	def __call__(   self						:Tuple	,  *a						:Optional[Any]	,  **a						:int		)						->					Dict:
								return super().__call__(*a	,  **a		)







	def   _lowerCamelCase							(   self						:Any	,  a						:int	,  a						:int	,  a						:int		)						->					bool:
								if max_length < min_length:
															logger.warning(f'Your min_length={min_length} must be inferior than your max_length={max_length}.'		)

								if input_length < max_length:
															logger.warning(
															    f'Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is '
															    "a summarization task, where outputs shorter than the input are typically wanted, you might "
															    f'consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})'		)




@add_end_docstrings(__lowercase)
class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	_A								=	'translation'







	def   _lowerCamelCase							(   self						:str	,  a						:int	,  a						:int	,  a						:int		)						->					Union[str, Any]:
								if input_length > 0.9 * max_length:
															logger.warning(
															    f'Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider '
															    "increasing your max_length manually, e.g. translator('...', max_length=400)"		)
								return True







	def   _lowerCamelCase							(   self						:List[Any]	,  *a						:List[Any]	,  a						:int=TruncationStrategy.DO_NOT_TRUNCATE	,  a						:Tuple=None	,  a						:Union[str, Any]=None		)						->					int:
								if getattr(self.tokenizer	,  "_build_translation_inputs"	,  a		):
															return self.tokenizer._build_translation_inputs(
															    *a	,  return_tensors=self.framework	,  truncation=a	,  src_lang=a	,  tgt_lang=a		)
								else:
															return super()._parse_and_tokenize(*a	,  truncation=a		)







	def   _lowerCamelCase							(   self						:Optional[Any]	,  a						:Tuple=None	,  a						:Union[str, Any]=None	,  **a						:List[str]		)						->					str:
								__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase      :  List[str]       =   super()._sanitize_parameters(**a		)
								if src_lang is not None:
															__UpperCamelCase      :  int       =   src_lang
								if tgt_lang is not None:
															__UpperCamelCase      :  List[Any]       =   tgt_lang
								if src_lang is None and tgt_lang is None:
															# Backward compatibility, direct arguments use is preferred.
															__UpperCamelCase      :  Dict       =   kwargs.get("task"	,  self.task		)
															__UpperCamelCase      :  str       =   task.split("_"		)
															if task and len(a		) == 4:
																						# translation, XX, to YY
																						__UpperCamelCase      :  List[str]       =   items[1]
																						__UpperCamelCase      :  Optional[Any]       =   items[3]
								return preprocess_params, forward_params, postprocess_params







	def __call__(   self						:Dict	,  *a						:Union[str, Any]	,  **a						:Optional[int]		)						->					List[Any]:
								return super().__call__(*a	,  **a		)

def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     int = 10**9)   ->       int:






							'''simple docstring'''





							__UpperCamelCase      :  int       =   1
							__UpperCamelCase      :  Any       =   2

							__UpperCamelCase      :  Dict       =   0
							__UpperCamelCase      :  Any       =   0
							__UpperCamelCase      :  List[Any]       =   0
							while perimeter <= max_perimeter:
														perimeters_sum += perimeter

														prev_value += 2 * value
														value += prev_value

														__UpperCamelCase      :  Optional[int]       =   2 * value + 2 if i % 2 == 0 else 2 * value - 2
														i += 1

							return perimeters_sum


if __name__ == "__main__":
		print(f"{solution() = }")